ML Session n°3

Transcript

1. ML: regression March 2017
2. Notations : Scalar (number) : Vector : Matrix : Transpose of matrix X : Mean of vectors x : Estimate of vector value x
3. Simple linear regression
4. Solving simple linear regression Goal : find the best and for Best ? Minimize the square residuals (least squares)
5. Solving simple linear regression: finding the best estimates for a and b
6. Demo 1
7. Multivariate linear regression
8. Demo 2
9. Polynomial regression
10. Trick : still a linear regression ! Just create additional columns, derived from pre-existing ones Then it comes back to a linear regression
11. Demo 3
12. Gradient descent
13. Gradient descent
14. Gradient descent
15. Normal equation vs gradient descent Normal equation Gradient descent No additional parameters Need to choose a learning step No loop Needs to iterate : for inverse Slow if is large Works well when is large In practice : n < 10.000 ⇔ normal equation
16. Logistic regression Used for binary classification Decision boundary : 0.5 - y < 0.5 : class A - y >= 0.5 : class B
17. Logistic regression : cost function
18. Logistic regression : cost function
19. Demo 4: logistic regression + gradient descent
20. Questions? March 2017